{
  "data": {
    "a": {
      "slug": "ling-3-1-flash-1019",
      "name": "Ling-3.1-flash-1019",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ling-3.1-flash-1019 is inclusionAI's open-weight hybrid reasoning mixture of experts with 560B parameters and 25B active, a post-trained update of Ling-3.1-flash, with switchable thinking, tool calling and a 262,144-token native context, published in FP8 under MIT.",
      "url": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019",
      "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json",
      "dns": "ling-3-1-flash-1019.grade.anchorterminal.com",
      "repo": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.1-flash-1019"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repository holds no LICENSE file). Running it yourself costs the hardware, 573.8 GB of FP8 files, which the card serves on 8 H200 or 4 GB300 GPUs (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "hybrid-attention",
        "reasoning",
        "tool-calling",
        "long-context",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-11-04",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 94,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.",
        "bestFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "strengths": [
          "Plain MIT, ungated, with a hash for every shard",
          "Released vLLM, SGLang, Transformers and llama.cpp all have the architecture",
          "A 262,144-token native context, and 1M with YaRN per SGLang's cookbook",
          "A training-content summary that names source categories, the size band and a collection date",
          "Updated a week before this check"
        ],
        "weaknesses": [
          "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it",
          "The card says 1M tokens of context where the files give 262,144",
          "The tool-call format is only in the template",
          "No safety results, no agent guidance and no security contact",
          "No inference provider mapped for this release, only for the earlier one",
          "Neither question on the repository has a reply"
        ],
        "agentNotes": [
          "Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows",
          "With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`",
          "Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook",
          "Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook",
          "Use temperature 1.0, top_p 0.95 and top_k 20, per the card",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# with SGLang 0.5.23 or later, on 8 H200 GPUs:\npython3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 \\\n  --tp 8 --ep 8 --speculative-algorithm NEXTN --host 0.0.0.0 --port 30000\n# or with vLLM 0.33.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.1-flash-1019 --trust-remote-code --tensor-parallel-size 8 \\\n  --enable-prefix-caching --enable-expert-parallel --mamba-cache-mode align \\\n  --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Ling-3.1-flash-1019-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"inclusionAI/Ling-3.1-flash-1019\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ling-3-1-flash-1019"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "lbs-27b",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "builtOn": "inclusionAI/Ling-3.1-flash, post-trained further (named on the card; the metadata names no base model)",
        "params": "560B (25B active)",
        "context": 262144,
        "goodFor": "Large hybrid reasoning model for agents, office work and software engineering on a multi-GPU server, with thinking switchable per request. It needs about 690 GB of GPU memory in FP8.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "bartowski Ling-3.1-flash-1019-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Ling-3.1-flash-1019-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 357.97,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not inclusionAI's. 357.97 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Ling-3.1-flash-1019",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 573.78,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The release itself, FP8 E4M3 weights in 128×128 blocks. inclusionAI publishes no smaller build"
          }
        ],
        "run": "python3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 --tp 8 --ep 8 --speculative-algorithm NEXTN",
        "runBuild": "Ling-3.1-flash-1019",
        "checked": "2026-11-10",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "Ling 3",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "Ling-3.0-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-Fin",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-VL",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL",
            "params": "124B total, 5.5B active"
          },
          {
            "name": "Ling-3.0-flash-dspark",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark",
            "params": "1.36B (draft)"
          },
          {
            "name": "Ling-3.0-flash-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe",
            "params": "5.18M (probe)"
          },
          {
            "name": "Ling-3.0-tiny",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
            "params": "7.9B total, 1.3B active",
            "listing": "https://www.anchorterminal.com/tools/ling-3-0-tiny"
          },
          {
            "name": "Ling-3.0-tiny-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe",
            "params": "3.22M (probe)"
          },
          {
            "name": "Ling-3.1-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash",
            "params": "560B total, 25B active"
          },
          {
            "name": "Ling-3.1-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-base",
            "params": "560B total, 25B active"
          }
        ],
        "olderCount": 54
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.",
          "Nothing is hosted by the publisher, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 8 commits from 27 October to 4 November 2026, the last three card edits.",
          "www.inclusion-ai.org/.well-known/security.txt answered 404 on 10 November 2026."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.json"
    },
    "answer": "Nanbeige4.3-33B scores 60.8 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "nanbeige-4-3-33b",
      "name": "Nanbeige4.3-33B",
      "vendor": "Nanbeige LLM Lab (BOSS Zhipin)",
      "vendorUrl": "https://huggingface.co/Nanbeige",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nanbeige4.3-33B is the Nanbeige LLM Lab's open-weight dense model for agentic coding and office work, 33B parameters on the Llama architecture with a 512K-token context, thinking that switches per request and tool calls in an XML format.",
      "url": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json",
      "dns": "nanbeige-4-3-33b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
      "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.3-33B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for Nanbeige/Nanbeige4.3-33B and Nanbeige/Nanbeige4.3-33B-Base (checked 2026-10-25).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata names it. Neither repository holds a LICENSE file. Running it costs the hardware, 66.26 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-25).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-25).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "llama",
        "reasoning",
        "tool-calling",
        "coding",
        "agentic",
        "long-context",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-31",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 68,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 42,
          "interface": 85,
          "reach": 80,
          "risk": 60,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file.",
        "bestFor": "Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.",
        "strengths": [
          "A plain LlamaForCausalLM with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it",
          "Apache 2.0 by its metadata, ungated, safetensors only",
          "524,288 tokens of context set in config.json with no rope scaling",
          "Thinking switches per request, and preserve_thinking keeps earlier reasoning for agent loops",
          "Tool calls in an XML format the qwen3_coder parser reads, several per turn",
          "Nanbeige publishes INT4, INT8 and FP8 builds, and a member answered nine of the first 30 discussions"
        ],
        "weaknesses": [
          "Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs",
          "No Ollama or LM Studio build, and Nanbeige's smallest build (INT4) still needs about 27 GB",
          "No LICENSE file in either repository; Apache 2.0 rests on the metadata tag",
          "No safety evaluation, agent-use guidance, training data description or knowledge cut-off",
          "No hosted API or inference provider serves it"
        ],
        "agentNotes": [
          "Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers",
          "On a 32 GB GPU use Nanbeige/Nanbeige4.3-33B-INT4 with the same vLLM or SGLang flags; the INT8 and FP8 builds need about 43 GB",
          "Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes",
          "Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends",
          "Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card",
          "Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed",
          "Send one system message at the start only, as the template raises an error for a later one",
          "In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.8
          }
        ],
        "editorialScores": {
          "capability": 42,
          "interface": 85,
          "reach": 80,
          "risk": 60,
          "upkeep": 56
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "capability": "2026-11-03",
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM 0.32.0 or SGLang 0.5.22, as the card's commands (the card uses four GPUs with tensor parallelism)\nhf download Nanbeige/Nanbeige4.3-33B --local-dir Nanbeige4.3-33B\nvllm serve ./Nanbeige4.3-33B --served-model-name nanbeige --tensor-parallel-size 4 \\\n  --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder\n# or\npython -m sglang.launch_server --model-path ./Nanbeige4.3-33B --tp-size 4 \\\n  --reasoning-parser qwen3 --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Nanbeige_Nanbeige4.3-33B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"nanbeige\", \"messages\": [{\"role\": \"user\", \"content\": \"Which number is bigger, 9.11 or 9.8?\"}],\n       \"temperature\": 0.6, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nanbeige-4-3-33b"
      },
      "sameCompany": [
        "nanbeige-4-2-3b"
      ],
      "weights": {
        "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)",
        "builtOn": "Nanbeige/Nanbeige4.3-33B-Base (fine-tune, declared in the card's metadata; Apache 2.0 by its tag, no LICENSE file)",
        "params": "33B",
        "context": 524288,
        "goodFor": "A cousin for long agentic coding and office tasks on a 32 GB GPU at INT4 or about 96 GB at BF16, with a 512K context and per-request thinking.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "Nanbeige4.3-33B-INT4",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-INT4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 22.57,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Compressed-tensors 4-bit weights in groups of 32, whole repository 22.57 GB. 22.57 GB × 1.2 = 27.1 GB"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.3-33B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 20.79,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. 20.79 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Nanbeige4.3-33B-FP8",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 35.54,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Compressed-tensors FP8 with dynamic activation scales. An INT8 build (36.01 GB) is in Nanbeige/Nanbeige4.3-33B-INT8"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.3-33B-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.3-33B-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 35.2,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. 35.20 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Nanbeige4.3-33B (BF16)",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.26,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "Whole repository, 66.24 GB of weights in 14 shards. 66.26 GB × 1.2 = 79.5 GB. Nanbeige also publishes INT4, INT8 and FP8 builds; no Ollama, LM Studio or ggml-org build exists, and llama.cpp needs a GGUF converted with its own script or taken from someone else"
          }
        ],
        "run": "vllm serve Nanbeige/Nanbeige4.3-33B-INT4 --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder",
        "runBuild": "Nanbeige4.3-33B-INT4",
        "checked": "2026-11-03",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Nanbeige LLM Lab (BOSS Zhipin)",
        "family": "Nanbeige4.3",
        "url": "https://www.anchorterminal.com/open-weights/#nanbeige",
        "others": [
          {
            "name": "Nanbeige4.3-33B-Base",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base",
            "params": "33B"
          }
        ],
        "olderCount": 7
      },
      "area": "models",
      "provenance": {
        "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed",
        "domain": "zhipin.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-25",
        "notes": [
          "The card names the Nanbeige Team and nothing else. BOSS Zhipin is identified as the maker by the lab's Nanbeige4.2-3B report on arXiv (Nanbeige LLM Lab, Boss Zhipin), which links huggingface.co/Nanbeige, and by the chat template's default system prompt naming BOSS Zhipin as developer and trainer. The card's contact address is on kanzhun.com. Kanzhun Limited as BOSS Zhipin's operator wasn't confirmed first-hand, since ir.zhipin.com refused our reader. The domain's registration date wasn't checked.",
          "The Hugging Face organisation Nanbeige (Nanbeige LLM Lab) isn't verified by the Hub. The arXiv report's link to it is the publisher's own documentation pointing there.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history.",
          "www.zhipin.com/.well-known/security.txt answers 404 and kanzhun.com's redirects to the home page. BOSS Zhipin runs a security response centre at security.zhipin.com for reporting flaws in its products; nothing links it to the model."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.json",
      "live": {
        "slug": "nanbeige-4-3-33b",
        "securityTxt": {
          "url": "https://zhipin.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:28.506819752Z"
        },
        "domain": {
          "domain": "zhipin.com",
          "registered": "2003-11-11",
          "source": "https://rdap.verisign.com/com/v1/domain/zhipin.com",
          "checkedAt": "2026-11-08T15:03:54.452286646Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:18.096335669Z",
            "changedAt": "2026-11-10T16:22:18.096335669Z",
            "fingerprint": "245289df7021"
          }
        ],
        "updatedAt": "2026-11-10T16:22:18.096335669Z"
      }
    },
    "edges": [
      {
        "by": 25,
        "category": "interface",
        "leads": "nanbeige-4-3-33b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.",
          "b": "The chat template earns 20, Jinja in tokenizer_config.json. The tool-calling format earns 25, with the XML call format in the template and the qwen3_coder parser named for vLLM and SGLang. Trained tool calling earns 20, several calls per turn in the template. Structured output earns 0, as nothing mentions it. Reasoning control earns 10, thinking switched per request with the qwen3 reasoning parser named. Context earns 10 for 512K set in the config with no scaling."
        },
        "scores": {
          "a": 60,
          "b": 85
        }
      },
      {
        "by": 20,
        "category": "reach",
        "leads": "nanbeige-4-3-33b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.",
          "b": "Memory earns 20, since Nanbeige's INT4 build is 22.57 GB, about 27 GB with a fifth added, which fits 32 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all run the Llama architecture, and Ollama and LM Studio have no build. Builds earn 15 for Nanbeige's INT4, INT8 and FP8 builds of 31 October 2026. Terms earn 15 for Apache 2.0 with nothing added, though only the metadata names it. Download earns 10, with no gate. Hosted earns 0, as no provider or Nanbeige API serves it."
        },
        "scores": {
          "a": 60,
          "b": 80
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "nanbeige-4-3-33b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.",
          "b": "File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run. Provenance earns 15, the lab's own organisation as its arXiv report links it, with a hash for every file. Safety evaluation earns 0 and agent guidance 0, since neither card says anything on either. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for BOSS Zhipin's security response centre, which isn't linked to the model."
        },
        "scores": {
          "a": 45,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "inclusionAI (Ant Group)",
        "b": "Nanbeige LLM Lab (BOSS Zhipin)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT",
        "b": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-04",
        "b": "2026-10-31",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "Nanbeige4.3-33B scores 60.8 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Ling-3.1-flash-1019 or Nanbeige4.3-33B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "slug": "ling-3-1-flash-1019",
        "watchFor": "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it"
      },
      {
        "aheadOn": [
          "Reach, 80 against 60",
          "Risk, 60 against 45",
          "Agent interface, 85 against 60"
        ],
        "also": null,
        "goodFor": "Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.",
        "slug": "nanbeige-4-3-33b",
        "watchFor": "Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs"
      }
    ],
    "history": {
      "ling-3-1-flash-1019": null,
      "nanbeige-4-3-33b": [
        {
          "causes": [
            "product",
            "evidence"
          ],
          "date": "2026-11-03",
          "delta": -0.2,
          "from": 61,
          "fromGrade": "C",
          "to": 60.8,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019.json",
        "title": "Agents-A1-4B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b.json",
        "title": "Agents-A1-4B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019.json",
        "title": "Apertus v1.5 8B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b.json",
        "title": "Apertus v1.5 8B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019.json",
        "title": "Apodex-1.1-mini vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b.json",
        "title": "Apodex-1.1-mini vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.json",
        "title": "Arete-27B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-3-33b.json",
        "title": "Arete-27B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019.json",
        "title": "BigBang-v1 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b.json",
        "title": "BigBang-v1 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019.json",
        "title": "Carbon-X vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b.json",
        "title": "Carbon-X vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.json",
        "title": "Cola2 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-3-33b.json",
        "title": "Cola2 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019.json",
        "title": "Cosmos-Reason2-2B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b.json",
        "title": "Cosmos-Reason2-2B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b.json",
        "title": "Darwin-36B-Opus vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-1-flash-1019.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ling-3-1-flash-1019.json",
        "title": "Devstral Small 2 vs Ling-3.1-flash-1019",
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        "title": "Devstral Small 2 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-1-flash-1019.json",
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-1-flash-1019.json",
        "title": "EXAONE 4.5 33B vs Ling-3.1-flash-1019",
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      {
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        "title": "MiniCPM-V 4.7 35B-A3B vs Nanbeige4.3-33B",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it"
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-4b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-4b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-2b-instruct.json",
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct.json",
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint.json",
        "title": "Nanbeige4.3-33B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ripple-65b-preview.json",
        "title": "Nanbeige4.3-33B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-sensenova-loop-9b-preview.json",
        "title": "Nanbeige4.3-33B vs SenseNova-Loop-9B-Preview",
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b.json",
        "title": "Nanbeige4.3-33B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-step-3-7-flash.json",
        "title": "Nanbeige4.3-33B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-unlimited-ocr-2.json",
        "title": "Nanbeige4.3-33B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-unlimited-ocr-2"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.json",
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        "nanbeige-4-3-33b": 60,
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      "nanbeige-4-3-33b": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file."
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    "slim": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nanbeige-4-3-33b.min.md"
  },
  "markdown": "Nanbeige4.3-33B scores 60.8 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Ling-3.1-flash-1019: grade C, 54.8/100, rank #126 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json\n- Nanbeige4.3-33B: grade C, 60.8/100, rank #86 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Ling-3.1-flash-1019 (C)\n\nGood for: Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding\n\nWatch for: 574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it\n\n### Nanbeige4.3-33B (C)\n\nGood for: Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops.\n\nAhead on:\n- Reach, 80 against 60\n- Risk, 60 against 45\n- Agent interface, 85 against 60\n\nWatch for: Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs\n\n\n## Why the edge\n\n### Agent interface: Nanbeige4.3-33B +25\n\n- Ling-3.1-flash-1019 (60): Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- Nanbeige4.3-33B (85): The chat template earns 20, Jinja in tokenizer_config.json. The tool-calling format earns 25, with the XML call format in the template and the qwen3_coder parser named for vLLM and SGLang. Trained tool calling earns 20, several calls per turn in the template. Structured output earns 0, as nothing mentions it. Reasoning control earns 10, thinking switched per request with the qwen3 reasoning parser named. Context earns 10 for 512K set in the config with no scaling.\n\n### Reach: Nanbeige4.3-33B +20\n\n- Ling-3.1-flash-1019 (60): Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.\n- Nanbeige4.3-33B (80): Memory earns 20, since Nanbeige's INT4 build is 22.57 GB, about 27 GB with a fifth added, which fits 32 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all run the Llama architecture, and Ollama and LM Studio have no build. Builds earn 15 for Nanbeige's INT4, INT8 and FP8 builds of 31 October 2026. Terms earn 15 for Apache 2.0 with nothing added, though only the metadata names it. Download earns 10, with no gate. Hosted earns 0, as no provider or Nanbeige API serves it.\n\n### Risk: Nanbeige4.3-33B +15\n\n- Ling-3.1-flash-1019 (45): File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n- Nanbeige4.3-33B (60): File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run. Provenance earns 15, the lab's own organisation as its arXiv report links it, with a hash for every file. Safety evaluation earns 0 and agent guidance 0, since neither card says anything on either. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for BOSS Zhipin's security response centre, which isn't linked to the model.\n\n## Score by category\n\n| Category | Weight | Ling-3.1-flash-1019 | Nanbeige4.3-33B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 80 | Nanbeige4.3-33B +20 |\n| Risk | 20% (20 this run) | 45 | 60 | Nanbeige4.3-33B +15 |\n| Agent interface | 10% (10 this run) | 60 | 85 | Nanbeige4.3-33B +25 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 56 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.8 · C** | **60.8 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.1-flash-1019 | Nanbeige4.3-33B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | Nanbeige LLM Lab (BOSS Zhipin) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-04 | 2026-10-31 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## How the scores have moved\n\n### Ling-3.1-flash-1019\n\nNo score change since it was listed.\n\n### Nanbeige4.3-33B\n\n- -0.2 on 3 November 2026: product, new evidence\n\n## Verdicts\n\n**Ling-3.1-flash-1019.** inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.\n\n**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file.\n\n## Before you call either\n\n### Ling-3.1-flash-1019\n\n1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows\n2. With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`\n3. Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook\n4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook\n5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card\n6. Validate JSON output yourself, since structured output isn't documented\n\n### Nanbeige4.3-33B\n\n1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers\n2. On a 32 GB GPU use Nanbeige/Nanbeige4.3-33B-INT4 with the same vLLM or SGLang flags; the INT8 and FP8 builds need about 43 GB\n3. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes\n4. Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends\n5. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card\n6. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed\n7. Send one system message at the start only, as the template raises an error for a later one\n8. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed\n\n## Questions\n\n### Which is better for AI agents, Ling-3.1-flash-1019 or Nanbeige4.3-33B?\n\nNanbeige4.3-33B scores 60.8 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nanbeige-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nanbeige-4-3-33b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-1-flash-1019\", \"b\": \"nanbeige-4-3-33b\"}`. 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    "title": "Ling-3.1-flash-1019 vs Nanbeige4.3-33B for AI agents (2026)",
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